When to Use a LoRA vs a Fixed Model

A LoRA is a small, custom-trained adapter that teaches an open image model a specific subject or style. It sounds like the professional path, and for a narrow set of cases it is. For the overwhelming majority of AI influencer and UGC work, though, a fixed hosted model like GPT Image 2 or Nano Banana 2 is the better tool, and it is not close.

Start with a fixed model (almost always)

Fixed hosted models need no training, no dataset, and no GPU time. You get state-of-the-art quality instantly, you can change look and scene with a prompt, and identity stays locked through a reference image. They cover the vast majority of looks, locations, wardrobes, and styles you will ever need for a persona feed. For most creators, the right number of LoRAs to train is zero.

• No training, no dataset, no GPU cost.
• Top-tier quality and realism out of the box.
• Restyle and re-scene instantly through prompting.
• Identity held by a reference, not a baked-in checkpoint.

When a LoRA actually earns its keep

A custom LoRA is worth the training cost in one situation: when you need output that general-purpose hosted models will not produce. That can be a hyper-specific aesthetic you cannot reach through prompting, or content that falls outside what hosted, general-purpose models are willing to generate. If your use case lives there, a LoRA on an open model is the only path. If it does not, training one is slower, more expensive, and lower quality than just prompting a fixed model well.

Be honest about which bucket you are in. Most "I need a LoRA" instincts are really "I have not learned to prompt the fixed model yet." Spend the effort on prompt structure first; it pays off across every model.

If you do go fixed: which one?

GPT Image 2 is the stronger realism engine but rewards descriptive, careful prompting and generates at lower resolutions. Nano Banana 2 supports resolutions up to 4K and excels at identity-locked edits from a reference, with a slightly more polished, more "AI" finish. The full breakdown is in the model comparison guide.